PT on CF
- 1. PredictiveGrid™ Mary 18, 2016 Company Confidential
- 2. Successful approval of Seed C term sheet which is reset of cap table and control structure (full support of GE Ventures and Investors in the Seed C Round) positioning us well operationally and for next Series A Round $250K DOE grant awarded to us to develop our products and commercialize for market (NOT a research award) PG&E, contract signed for paid Proof of concepts - increase Paid-Proof of Concept Demonstrability (3 new use cases- total $125K) Preferred Vendor Status Contract Signed with PG&E - Allowing PingThings to conduct business with all other business units PG&E recommended us to their Distribution group for more paid product engagements -building our modules tailored to this new group (have immediate budget in place for immediate engagement) Major Data Sharing Agreements signed – We are getting data from 20% of all utilities in North America and estimate we will be getting data from 40% in Q4.(Peak Reliability Contract signed, ISO New England Contract signed, more in discussion) Starting Multi year commercial (beta) license deal discussions ($950K - $1.2M each) with Peak RC, PG&E and other major utilities/ISOs (Central Main Power, Idaho Power..etc). Completed military grade colocation facility – data security audited and approved by Peak Reliability Coordinator and PG&E. Continuing Thought Leadership in the industry- published O’Reilly technology book in March “Data and Electric Power- From Deterministic Machines to Probabilistic Systems in Traditional Engineering” Business update- last 90 days
- 3. $800B Invested in Smart Grid infrastructure in the past decade 4 Existing technology is exploiting only a fraction of the data’s potential value Intelligent, transmission sensors generate massive amounts of high fidelity, high volume data Uses SmartGrid data to provide real time, pinpoint visibility into critical assets and environments, for multi-million dollar cost savings and transformational insights
- 4. Aging Physical Infrastructure Break/Fix Approach Electric Utility Challenges 3 Utility Industry is $1T in US alone with $3T in assets, Unplanned utility outages cost $180B per year Explosion of New Data Company Confidential
- 5. 4Company confidential Market Status A PROACTIVE solution to one opportunity - Intelligent Asset Maintenance - will save $50B per year in the US Intelligent Asset Maintenance Asset Protection Solar Flare + GMD Impact Security (EMP) Intelligence Distributed Generation Disruptions Islanding Dashboards BI & Reporting Historical Analytics Legacy solutions are REACTIVE Opportunities
- 6. Leveraging IoT Investment PingThings uses existing end-points and data streams to create new IoT Applications Uses Big Data Science to identify anomalies, create alerts and recommend actions from existing data streams and external data Creates an Anomaly Detection Engine that monitors the same data streams in real time to identify events, alerts and corrective actions That is continuously refined through ongoing Machine Learning Empowers operators with tools to mitigate disruptive events before they happen Makes use of current under-utilized data instead of adding additional sensors, networks and data management 5
- 7. High demand for analytics North American Smart Grid Market Growth 2013-2014 Source: GTM Research 2013-2014 Growth Rate (CAGR) US transformer monitoring analytics alone will grow 8x in 5 years 6Company Confidential
- 8. Traction 7 Company Confidential Investors Proof of Concepts Research Partners
- 9. Who We Are 8 Company Confidential Rich Sootkoos Chief Executive Officer Jerry Schuman Chief Technology Officer Sean Patrick Murphy Chief Data Scientist 3x Founder/Co- Founder with Successful Exits Senior Executive Activision, ZeroDegrees, Idealab, Disney, PepsiCo Senior Scientist Johns Hopkins University Applied Physics Advanced Machine Learning 3x Founder/Co- Founder Sun Microsystems, Apple, Canon Mehrdod Mohseni Chief Revenue Officer Chief Marketing Officer GE Energy President UISOL, an Alstom Grid Company Registered Professional Engineer and Senior IEEE member
- 10. 5 Ingest Engine Receives & processes data PhasorSense™ Specific Smart Grid data science algorithms combined with machine learning Saving utility companies tens of millions by reducing the need for additional hardware and/or expensive field based site activity Initial Use Cases: Asset Maintenance, Asset Protection, Asset Performance, Sun Flares, Weather events, Hydro/wind/solar impacts PredictiveGrid™ Assessments & alerts for pinpoint visibility Transforming Transmission Operations 9
- 11. 10Company Confidential SCADA PMU OTHERS HISTORIAN & PDC’s Stream SPS EMS OMS EMS- Energy Management System SPS- Special Protection Systems OMS- Outage Management Systems Ingest Engine Ingest Engine Receives & processes data Receives streaming and historical data from multiple sources and makes it available for analytics
- 12. 11Company Confidential Our technology can identify and predict events using existing high fidelity synchrophasor data without the need for additional hardware sensors deployments (see appendix) Classifiers Anomaly Detection Machine Learning PhasorSenseTM Patent-pending technology combines data science, machine learning, and algorithms to analyze grid specific data PingThings Predictive Analytics Actual readings from costly atypical hardware deployed for this specific event
- 13. 12Company Confidential Identifies priorities for action, in advance, without field deployment. Delivers actionable reports & alerts. PredictiveGrid™
- 14. PredictiveGrid:GIC PMU Stream Processing Typical Hardware requirements to support PingThings Analytics • Apache Spark Streaming (compute fabric) • Kafka (message bus) • Cassandra (time-series persistence) • nVidia Tesla GPU enabled “deep learning” • GE Predix™ Machine Enabled • C37.118.2 Protocol support • Space Weather Prediction Center Alert Engine • TPL-007-1 Compliance Assistance Simple Installation/Integration 13
- 15. Faster sales cycle Faster adoption/penetration • Current collaboration with DOE and interconnect entities (RRO- Regional Reliability Organizations) that monitor large groups of utilities Very high value to cost ratio at current expectations from large utilities • Does not require a system change • Significantly lower price point than current utility platforms • Hyper-growth of data can be leveraged with minimal additional costs Low risk / high reward • Pure software play requiring no new hardware and independent of existing systems • Appliance loosely-coupled architecture for easy assimilated plug and play FERC orders & mandates • Compliance (geomagnetic disruption / solar flares) Company Confidential 15 Sales cycle reduced from 3 years to 6 months
- 16. Rich Sootkoos, CEO rich@pingthings.io Company Confidential